FREN Discuss a mission

AI SYSTEMS ENGINEERING / KNOWLEDGE BASE

Enter through the problem. Leave with a system-level decision

Thirty-six technical guides connect the vocabulary teams search for to architecture, measurement, evidence and authority. No generic AI glossary: each page ends in an engineering decision.

Inspect executed results Bring a mission

01 / AI systems architecture

The engineering object is the complete realization. A model is only one possible component among data, memory, routing, tools, rules, verifiers and runtime.

Enterprise AI architecture: engineer the complete system

How to design an enterprise AI architecture connecting models, data, memory, tools, verification, authority and operations.

AI system audit: measure before replacing

A full AI system audit method covering baselines, failure maps, dependencies, costs, evidence and transformation decisions.

AI system transformation: from model to realization

Transform an existing AI system through measured specialization, composition, verification, rematerialization and qualification.

Production AI architecture: identity, control and recovery

Production AI architecture requirements: exact identity, observability, controls, refusal, rollback and requalification.

Hybrid AI architecture: neural, deterministic and verifiable

Compose neural models, retrieval, rules, solvers and verifiers without inferring system safety from component safety.

Composable AI systems: interfaces, state and evidence

Design composable AI systems with interface contracts, explicit state, routing, failure isolation and composition evidence.

02 / Private and sovereign AI

Privacy and sovereignty are properties of the executed system: network flows, data, telemetry, dependencies, updates, backups and authority must share one controlled perimeter.

Private LLM: beyond a locally hosted model

Deploy a private LLM with controlled data, runtime, observability, authority and explicitly bounded external dependencies.

On-premise AI: architecture, cost and operational control

Design on-premise AI by evaluating hardware, models, data, updates, observability, security and total operating cost.

Sovereign AI: control more than hosting location

Sovereign AI architecture must control identity, data, runtime, models, decisions, dependencies and replacement capability.

Air-gapped AI: operate without network dependencies

Design isolated AI with transportable artifacts, integrity verification, controlled updates and local logging.

Local AI deployment: choose a fitting realization

Deploy AI locally by aligning model, quantization, memory, context, throughput, privacy and fallback behavior.

Private AI without cloud APIs: what must be isolated

Build private AI without external APIs by controlling inference, embeddings, retrieval, telemetry, updates and logs.

03 / AI evaluation and observability

Evaluating an AI system binds a capability, an exact object, a protocol, observations and a decision rule. An isolated score is not a qualification.

LLM evaluation: measure the capability actually required

Evaluate an LLM with behavior contracts, protected sets, OOD robustness, schema accuracy and baseline comparison.

RAG evaluation: retrieval, grounding and final answer

Evaluate RAG by separating retrieval quality, coverage, source fidelity, generation, refusal and cost.

AI agent evaluation: trajectories, tools and authority

Evaluate AI agents beyond final output: planning, tool calls, state, cost, permissions, recovery and refusal.

AI reliability: bound behavior instead of promising absolutes

Engineer AI reliability with failure scenarios, OOD tests, repetitions, refusal, verification and declared limits.

AI observability: bind every output to its execution

Implement useful AI observability with release identity, bounded inputs, decisions, tools, latency, cost and failures.

AI system benchmarking: compare complete realizations

Compare AI systems across quality, robustness, latency, memory, cost, dependencies and failure behavior.

04 / Efficiency and substrate affinity

Cost is reduced at realization level: avoiding calls, shortening context, quantizing, routing or rematerializing a responsibility can matter more than isolated model optimization.

Reduce LLM inference costs at system level

Reduce LLM cost through routing, context control, quantization, caching, specialization and deterministic rematerialization.

Run an LLM on CPU: method and limits

Evaluate CPU LLM inference with quantization, memory, context, throughput, concurrency and explicit capability gates.

CPU-native AI: distribute responsibilities without a GPU

Design CPU-native AI with a small model, rules, retrieval, verification and deterministic decisions.

Small models vs large models: decide through evidence

Compare small and large models across capability, robustness, latency, memory, cost and system role.

LLM distillation: transfer capability without assuming equivalence

Use distillation to transfer a bounded capability into a smaller model, then test robustness and residual losses.

Model quantization: measure the cost of compression

Choose quantization by measuring memory, throughput, latency, fidelity, robustness and runtime compatibility.

05 / AI control and authority

Capability does not grant authority. Critical systems must separate what proposes, verifies, permits and executes.

Deterministic AI verification: control critical actions

Separate probabilistic proposals from deterministic validation for schemas, amounts, policies and critical actions.

AI authority gates: who may propose, decide and execute?

Design authority gates that separate proposal, permission, execution and logging in an AI system.

Fail-closed AI: refuse when evidence is missing

Implement fail-closed behavior with required evidence, strict schemas, timeouts and explicit refusals.

Out-of-distribution evaluation: test what the system did not learn

Build OOD evaluation that measures generalization, unknown detection, refusal and collateral regressions.

Reduce AI hallucinations through architecture

Reduce hallucinations with grounding, schemas, verification, refusal, authority separation and causal measurement.

06 / AMF evidence, provenance and research

Claims should terminate in inspectable evidence. Identity, provenance, measurement, adjudication, qualification and authority remain distinct dimensions.

Evidence-based AI: from claim to receipt

Bind every AI claim to a measurement, identity, protocol, evidence role and explicit scope limit.

Qualified AI systems: a bounded verdict, not a universal label

Understand AMF qualification: exact realization, mission, admissible evidence, frozen rule, verdict and limited authority.

AI system lineage: reconstruct every transformation

Trace parents, data, operators, artifacts, measurements and decisions to reconstruct an AI realization lineage.

AI model provenance: weights, data and claim limits

Document model provenance with pinned parent, dataset receipts, code, parameters, licenses and transformations.

AI Mission Contract: freeze the objective before technology

Define objective, constraints, prohibitions, budget, evidence sets, hard gates and stopping rules before experimentation.

Capability rematerialization: change substrate under contract

Test whether an observed capability can be rebuilt in a model, rule, retrieval system, solver or different composition.

Make the Transformer optional without anti-model dogma

Use a Transformer as component, teacher or scientific source, then test whether it must survive in the final realization.